Overview

Dataset statistics

Number of variables43
Number of observations827
Missing cells15311
Missing cells (%)43.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory244.0 KiB
Average record size in memory302.2 B

Variable types

Numeric18
DateTime1
Categorical9
Boolean15

Warnings

aptt has a high cardinality: 152 distinct values High cardinality
fibrinogen has a high cardinality: 117 distinct values High cardinality
haematocrit_percent is highly correlated with hemoglobinHigh correlation
hemoglobin is highly correlated with haematocrit_percentHigh correlation
day_from_enrolment is highly correlated with day_from_admissionHigh correlation
day_from_admission is highly correlated with day_from_enrolmentHigh correlation
age has 26 (3.1%) missing values Missing
albumin has 789 (95.4%) missing values Missing
alt has 766 (92.6%) missing values Missing
aptt has 237 (28.7%) missing values Missing
ast has 766 (92.6%) missing values Missing
bleeding has 339 (41.0%) missing values Missing
bleeding_vaginal has 348 (42.1%) missing values Missing
creatinine has 783 (94.7%) missing values Missing
echymose has 715 (86.5%) missing values Missing
echymosis has 739 (89.4%) missing values Missing
event_serology has 652 (78.8%) missing values Missing
fibrinogen has 237 (28.7%) missing values Missing
gender has 26 (3.1%) missing values Missing
haematocrit_percent has 265 (32.0%) missing values Missing
hematemesis has 715 (86.5%) missing values Missing
hematoma has 715 (86.5%) missing values Missing
hemoglobin has 266 (32.2%) missing values Missing
igg has 654 (79.1%) missing values Missing
igg_interpretation has 654 (79.1%) missing values Missing
igm has 652 (78.8%) missing values Missing
igm_interpretation has 652 (78.8%) missing values Missing
lymphocytes_percent has 267 (32.3%) missing values Missing
meche has 715 (86.5%) missing values Missing
melaena has 715 (86.5%) missing values Missing
neutrophils_percent has 266 (32.2%) missing values Missing
pcr_dengue_load has 713 (86.2%) missing values Missing
pcr_dengue_serotype has 17 (2.1%) missing values Missing
plt has 241 (29.1%) missing values Missing
pt has 262 (31.7%) missing values Missing
serology_interpretation has 757 (91.5%) missing values Missing
wbc has 266 (32.2%) missing values Missing
weight has 37 (4.5%) missing values Missing
day_from_enrolment has 26 (3.1%) missing values Missing
day_from_admission has 26 (3.1%) missing values Missing
day_from_enrolment has 88 (10.6%) zeros Zeros
day_from_admission has 88 (10.6%) zeros Zeros

Reproduction

Analysis started2021-02-12 10:51:24.273308
Analysis finished2021-02-12 10:51:55.987013
Duration31.71 seconds
Software versionpandas-profiling v2.10.0
Download configurationconfig.yaml

Variables

study_no
Real number (ℝ≥0)

Distinct112
Distinct (%)13.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.86094317
Minimum1
Maximum225
Zeros0
Zeros (%)0.0%
Memory size6.6 KiB
2021-02-12T10:51:56.061544image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q128.5
median72
Q3154
95-th percentile191
Maximum225
Range224
Interquartile range (IQR)125.5

Descriptive statistics

Standard deviation63.94259124
Coefficient of variation (CV)0.7534984747
Kurtosis-1.134883981
Mean84.86094317
Median Absolute Deviation (MAD)50
Skewness0.4867513233
Sum70180
Variance4088.654974
MonotocityIncreasing
2021-02-12T10:51:56.171965image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8016
 
1.9%
16115
 
1.8%
8314
 
1.7%
7213
 
1.6%
15913
 
1.6%
7712
 
1.5%
6112
 
1.5%
3011
 
1.3%
7411
 
1.3%
7311
 
1.3%
Other values (102)699
84.5%
ValueCountFrequency (%)
19
1.1%
29
1.1%
311
1.3%
43
 
0.4%
59
1.1%
ValueCountFrequency (%)
2251
0.1%
2241
0.1%
2231
0.1%
2221
0.1%
2211
0.1%

date
Date

Distinct211
Distinct (%)25.7%
Missing7
Missing (%)0.8%
Memory size6.6 KiB
Minimum2010-06-12 00:00:00
Maximum2012-04-04 00:00:00
2021-02-12T10:51:56.272228image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:56.387586image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

age
Real number (ℝ≥0)

MISSING

Distinct14
Distinct (%)1.7%
Missing26
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean18.85892634
Minimum12
Maximum25
Zeros0
Zeros (%)0.0%
Memory size6.6 KiB
2021-02-12T10:51:56.480316image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile13
Q116
median18
Q323
95-th percentile25
Maximum25
Range13
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.985451461
Coefficient of variation (CV)0.2113297114
Kurtosis-1.218488173
Mean18.85892634
Median Absolute Deviation (MAD)4
Skewness0.03882150443
Sum15106
Variance15.88382335
MonotocityNot monotonic
2021-02-12T10:51:56.566761image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
18120
14.5%
16109
13.2%
2574
8.9%
2372
8.7%
2470
8.5%
1456
6.8%
1349
 
5.9%
2249
 
5.9%
1739
 
4.7%
2137
 
4.5%
Other values (4)126
15.2%
ValueCountFrequency (%)
1234
 
4.1%
1349
5.9%
1456
6.8%
1528
 
3.4%
16109
13.2%
ValueCountFrequency (%)
2574
8.9%
2470
8.5%
2372
8.7%
2249
5.9%
2137
4.5%

albumin
Real number (ℝ≥0)

MISSING

Distinct33
Distinct (%)86.8%
Missing789
Missing (%)95.4%
Infinite0
Infinite (%)0.0%
Mean31.43421053
Minimum18.3
Maximum42.3
Zeros0
Zeros (%)0.0%
Memory size6.6 KiB
2021-02-12T10:51:56.655738image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum18.3
5-th percentile20.995
Q127.1
median32.45
Q337.25
95-th percentile40.26
Maximum42.3
Range24
Interquartile range (IQR)10.15

Descriptive statistics

Standard deviation6.626412193
Coefficient of variation (CV)0.2108025645
Kurtosis-0.9974062732
Mean31.43421053
Median Absolute Deviation (MAD)4.9
Skewness-0.3016566273
Sum1194.5
Variance43.90933855
MonotocityNot monotonic
2021-02-12T10:51:56.763261image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
21.13
 
0.4%
37.32
 
0.2%
27.82
 
0.2%
24.72
 
0.2%
27.41
 
0.1%
39.81
 
0.1%
271
 
0.1%
30.11
 
0.1%
31.21
 
0.1%
36.31
 
0.1%
Other values (23)23
 
2.8%
(Missing)789
95.4%
ValueCountFrequency (%)
18.31
 
0.1%
20.41
 
0.1%
21.13
0.4%
22.81
 
0.1%
24.72
0.2%
ValueCountFrequency (%)
42.31
0.1%
40.61
0.1%
40.21
0.1%
39.81
0.1%
391
0.1%

alt
Real number (ℝ≥0)

MISSING

Distinct56
Distinct (%)91.8%
Missing766
Missing (%)92.6%
Infinite0
Infinite (%)0.0%
Mean185.5409836
Minimum10
Maximum1414
Zeros0
Zeros (%)0.0%
Memory size6.6 KiB
2021-02-12T10:51:56.865482image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile18
Q135
median86
Q3172
95-th percentile635
Maximum1414
Range1404
Interquartile range (IQR)137

Descriptive statistics

Standard deviation282.6606077
Coefficient of variation (CV)1.523440278
Kurtosis10.26389794
Mean185.5409836
Median Absolute Deviation (MAD)56
Skewness3.059797111
Sum11318
Variance79897.01913
MonotocityNot monotonic
2021-02-12T10:51:56.988904image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1372
 
0.2%
272
 
0.2%
212
 
0.2%
612
 
0.2%
852
 
0.2%
2591
 
0.1%
991
 
0.1%
421
 
0.1%
2911
 
0.1%
1021
 
0.1%
Other values (46)46
 
5.6%
(Missing)766
92.6%
ValueCountFrequency (%)
101
0.1%
111
0.1%
121
0.1%
181
0.1%
201
0.1%
ValueCountFrequency (%)
14141
0.1%
13911
0.1%
7771
0.1%
6351
0.1%
6231
0.1%

aptt
Categorical

HIGH CARDINALITY
MISSING

Distinct152
Distinct (%)25.8%
Missing237
Missing (%)28.7%
Memory size6.6 KiB
335 
38.1
 
8
35.9
 
5
36.1
 
4
36.8
 
4
Other values (147)
234 

Length

Max length6
Median length1
Mean length2.261016949
Min length1

Characters and Unicode

Total characters1334
Distinct characters13
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique84 ?
Unique (%)14.2%

Sample

1st row
2nd row33.2
3rd row
4th row36.2
5th row
ValueCountFrequency (%)
335
40.5%
38.18
 
1.0%
35.95
 
0.6%
36.14
 
0.5%
36.84
 
0.5%
324
 
0.5%
32.84
 
0.5%
35.54
 
0.5%
29.64
 
0.5%
36.43
 
0.4%
Other values (142)215
26.0%
(Missing)237
28.7%
2021-02-12T10:51:57.231018image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
38.18
 
3.1%
35.95
 
2.0%
29.64
 
1.6%
36.14
 
1.6%
324
 
1.6%
36.84
 
1.6%
35.54
 
1.6%
32.84
 
1.6%
38.43
 
1.2%
34.93
 
1.2%
Other values (139)212
83.1%

Most occurring characters

ValueCountFrequency (%)
337
25.3%
.238
17.8%
3211
15.8%
497
 
7.3%
276
 
5.7%
572
 
5.4%
662
 
4.6%
959
 
4.4%
158
 
4.3%
848
 
3.6%
Other values (3)76
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number757
56.7%
Space Separator337
25.3%
Other Punctuation240
 
18.0%

Most frequent character per category

ValueCountFrequency (%)
3211
27.9%
497
12.8%
276
 
10.0%
572
 
9.5%
662
 
8.2%
959
 
7.8%
158
 
7.7%
848
 
6.3%
739
 
5.2%
035
 
4.6%
ValueCountFrequency (%)
.238
99.2%
,2
 
0.8%
ValueCountFrequency (%)
337
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1334
100.0%

Most frequent character per script

ValueCountFrequency (%)
337
25.3%
.238
17.8%
3211
15.8%
497
 
7.3%
276
 
5.7%
572
 
5.4%
662
 
4.6%
959
 
4.4%
158
 
4.3%
848
 
3.6%
Other values (3)76
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII1334
100.0%

Most frequent character per block

ValueCountFrequency (%)
337
25.3%
.238
17.8%
3211
15.8%
497
 
7.3%
276
 
5.7%
572
 
5.4%
662
 
4.6%
959
 
4.4%
158
 
4.3%
848
 
3.6%
Other values (3)76
 
5.7%

ast
Real number (ℝ≥0)

MISSING

Distinct60
Distinct (%)98.4%
Missing766
Missing (%)92.6%
Infinite0
Infinite (%)0.0%
Mean463.3278689
Minimum14
Maximum9999
Zeros0
Zeros (%)0.0%
Memory size6.6 KiB
2021-02-12T10:51:57.329625image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile23
Q169
median162
Q3348
95-th percentile1187
Maximum9999
Range9985
Interquartile range (IQR)279

Descriptive statistics

Standard deviation1348.666659
Coefficient of variation (CV)2.91082568
Kurtosis43.33119985
Mean463.3278689
Median Absolute Deviation (MAD)112
Skewness6.292571635
Sum28263
Variance1818901.757
MonotocityNot monotonic
2021-02-12T10:51:57.444437image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
272
 
0.2%
23291
 
0.1%
1681
 
0.1%
691
 
0.1%
3681
 
0.1%
3481
 
0.1%
2831
 
0.1%
421
 
0.1%
1081
 
0.1%
2181
 
0.1%
Other values (50)50
 
6.0%
(Missing)766
92.6%
ValueCountFrequency (%)
141
0.1%
151
0.1%
211
0.1%
231
0.1%
261
0.1%
ValueCountFrequency (%)
99991
0.1%
33361
0.1%
23291
0.1%
11871
0.1%
10811
0.1%

bleeding
Boolean

MISSING

Distinct2
Distinct (%)0.4%
Missing339
Missing (%)41.0%
Memory size6.6 KiB
True
365 
False
123 
(Missing)
339 
ValueCountFrequency (%)
True365
44.1%
False123
 
14.9%
(Missing)339
41.0%
2021-02-12T10:51:57.511557image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size955.0 B
False
716 
True
111 
ValueCountFrequency (%)
False716
86.6%
True111
 
13.4%
2021-02-12T10:51:57.546171image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size955.0 B
False
758 
True
 
69
ValueCountFrequency (%)
False758
91.7%
True69
 
8.3%
2021-02-12T10:51:57.578875image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Distinct5
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size6.6 KiB
nan
739 
1.0
 
35
3.0
 
25
2.0
 
21
4.0
 
7

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2481
Distinct characters8
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownan
2nd rownan
3rd rownan
4th rownan
5th row3.0
ValueCountFrequency (%)
nan739
89.4%
1.035
 
4.2%
3.025
 
3.0%
2.021
 
2.5%
4.07
 
0.8%
2021-02-12T10:51:57.748051image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-02-12T10:51:57.806634image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
nan739
89.4%
1.035
 
4.2%
3.025
 
3.0%
2.021
 
2.5%
4.07
 
0.8%

Most occurring characters

ValueCountFrequency (%)
n1478
59.6%
a739
29.8%
.88
 
3.5%
088
 
3.5%
135
 
1.4%
325
 
1.0%
221
 
0.8%
47
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2217
89.4%
Decimal Number176
 
7.1%
Other Punctuation88
 
3.5%

Most frequent character per category

ValueCountFrequency (%)
088
50.0%
135
 
19.9%
325
 
14.2%
221
 
11.9%
47
 
4.0%
ValueCountFrequency (%)
n1478
66.7%
a739
33.3%
ValueCountFrequency (%)
.88
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2217
89.4%
Common264
 
10.6%

Most frequent character per script

ValueCountFrequency (%)
.88
33.3%
088
33.3%
135
 
13.3%
325
 
9.5%
221
 
8.0%
47
 
2.7%
ValueCountFrequency (%)
n1478
66.7%
a739
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2481
100.0%

Most frequent character per block

ValueCountFrequency (%)
n1478
59.6%
a739
29.8%
.88
 
3.5%
088
 
3.5%
135
 
1.4%
325
 
1.0%
221
 
0.8%
47
 
0.3%

bleeding_vaginal
Boolean

MISSING

Distinct2
Distinct (%)0.4%
Missing348
Missing (%)42.1%
Memory size6.6 KiB
False
340 
True
139 
(Missing)
348 
ValueCountFrequency (%)
False340
41.1%
True139
 
16.8%
(Missing)348
42.1%
2021-02-12T10:51:57.853449image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

bruising
Boolean

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size955.0 B
False
493 
True
334 
ValueCountFrequency (%)
False493
59.6%
True334
40.4%
2021-02-12T10:51:57.887025image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

creatinine
Real number (ℝ≥0)

MISSING

Distinct33
Distinct (%)75.0%
Missing783
Missing (%)94.7%
Infinite0
Infinite (%)0.0%
Mean81.02272727
Minimum41
Maximum160
Zeros0
Zeros (%)0.0%
Memory size6.6 KiB
2021-02-12T10:51:57.957914image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum41
5-th percentile51.6
Q165.5
median79.5
Q389.5
95-th percentile136
Maximum160
Range119
Interquartile range (IQR)24

Descriptive statistics

Standard deviation24.92452667
Coefficient of variation (CV)0.3076238915
Kurtosis2.517919835
Mean81.02272727
Median Absolute Deviation (MAD)13
Skewness1.364861049
Sum3565
Variance621.2320296
MonotocityNot monotonic
2021-02-12T10:51:58.057693image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
813
 
0.4%
743
 
0.4%
912
 
0.2%
882
 
0.2%
792
 
0.2%
592
 
0.2%
692
 
0.2%
872
 
0.2%
662
 
0.2%
621
 
0.1%
Other values (23)23
 
2.8%
(Missing)783
94.7%
ValueCountFrequency (%)
411
0.1%
461
0.1%
511
0.1%
551
0.1%
561
0.1%
ValueCountFrequency (%)
1601
0.1%
1501
0.1%
1391
0.1%
1191
0.1%
1081
0.1%

echymose
Boolean

MISSING

Distinct2
Distinct (%)1.8%
Missing715
Missing (%)86.5%
Memory size6.6 KiB
False
109 
True
 
3
(Missing)
715 
ValueCountFrequency (%)
False109
 
13.2%
True3
 
0.4%
(Missing)715
86.5%
2021-02-12T10:51:58.123778image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

echymosis
Boolean

MISSING

Distinct2
Distinct (%)2.3%
Missing739
Missing (%)89.4%
Memory size6.6 KiB
False
85 
True
 
3
(Missing)
739 
ValueCountFrequency (%)
False85
 
10.3%
True3
 
0.4%
(Missing)739
89.4%
2021-02-12T10:51:58.164683image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

event_serology
Boolean

MISSING

Distinct2
Distinct (%)1.1%
Missing652
Missing (%)78.8%
Memory size6.6 KiB
True
174 
False
 
1
(Missing)
652 
ValueCountFrequency (%)
True174
 
21.0%
False1
 
0.1%
(Missing)652
78.8%
2021-02-12T10:51:58.198221image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

fibrinogen
Categorical

HIGH CARDINALITY
MISSING

Distinct117
Distinct (%)19.8%
Missing237
Missing (%)28.7%
Memory size6.6 KiB
335 
3.7
 
13
4.5
 
12
3.6
 
10
3.9
 
10
Other values (112)
210 

Length

Max length18
Median length1
Mean length2.306779661
Min length1

Characters and Unicode

Total characters1361
Distinct characters13
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique77 ?
Unique (%)13.1%

Sample

1st row
2nd row4
3rd row
4th row3.7
5th row
ValueCountFrequency (%)
335
40.5%
3.713
 
1.6%
4.512
 
1.5%
3.610
 
1.2%
3.910
 
1.2%
4.19
 
1.1%
3.59
 
1.1%
4.27
 
0.8%
2.97
 
0.8%
4.46
 
0.7%
Other values (107)172
20.8%
(Missing)237
28.7%
2021-02-12T10:51:58.396384image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3.714
 
5.5%
4.512
 
4.7%
3.610
 
3.9%
3.910
 
3.9%
3.59
 
3.5%
4.19
 
3.5%
2.97
 
2.7%
4.27
 
2.7%
3.86
 
2.4%
4.66
 
2.4%
Other values (105)165
64.7%

Most occurring characters

ValueCountFrequency (%)
337
24.8%
.245
18.0%
9134
 
9.8%
3113
 
8.3%
4110
 
8.1%
094
 
6.9%
580
 
5.9%
269
 
5.1%
747
 
3.5%
647
 
3.5%
Other values (3)85
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number777
57.1%
Space Separator337
24.8%
Other Punctuation247
 
18.1%

Most frequent character per category

ValueCountFrequency (%)
9134
17.2%
3113
14.5%
4110
14.2%
094
12.1%
580
10.3%
269
8.9%
747
 
6.0%
647
 
6.0%
147
 
6.0%
836
 
4.6%
ValueCountFrequency (%)
.245
99.2%
,2
 
0.8%
ValueCountFrequency (%)
337
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1361
100.0%

Most frequent character per script

ValueCountFrequency (%)
337
24.8%
.245
18.0%
9134
 
9.8%
3113
 
8.3%
4110
 
8.1%
094
 
6.9%
580
 
5.9%
269
 
5.1%
747
 
3.5%
647
 
3.5%
Other values (3)85
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1361
100.0%

Most frequent character per block

ValueCountFrequency (%)
337
24.8%
.245
18.0%
9134
 
9.8%
3113
 
8.3%
4110
 
8.1%
094
 
6.9%
580
 
5.9%
269
 
5.1%
747
 
3.5%
647
 
3.5%
Other values (3)85
 
6.2%

gender
Categorical

MISSING

Distinct2
Distinct (%)0.2%
Missing26
Missing (%)3.1%
Memory size6.6 KiB
Female
454 
Male
347 

Length

Max length6
Median length6
Mean length5.133583021
Min length4

Characters and Unicode

Total characters4112
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowFemale
3rd rowFemale
4th rowFemale
5th rowFemale
ValueCountFrequency (%)
Female454
54.9%
Male347
42.0%
(Missing)26
 
3.1%
2021-02-12T10:51:58.572310image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-02-12T10:51:58.627636image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
female454
56.7%
male347
43.3%

Most occurring characters

ValueCountFrequency (%)
e1255
30.5%
a801
19.5%
l801
19.5%
F454
 
11.0%
m454
 
11.0%
M347
 
8.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3311
80.5%
Uppercase Letter801
 
19.5%

Most frequent character per category

ValueCountFrequency (%)
e1255
37.9%
a801
24.2%
l801
24.2%
m454
 
13.7%
ValueCountFrequency (%)
F454
56.7%
M347
43.3%

Most occurring scripts

ValueCountFrequency (%)
Latin4112
100.0%

Most frequent character per script

ValueCountFrequency (%)
e1255
30.5%
a801
19.5%
l801
19.5%
F454
 
11.0%
m454
 
11.0%
M347
 
8.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII4112
100.0%

Most frequent character per block

ValueCountFrequency (%)
e1255
30.5%
a801
19.5%
l801
19.5%
F454
 
11.0%
m454
 
11.0%
M347
 
8.4%

haematocrit_percent
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct196
Distinct (%)34.9%
Missing265
Missing (%)32.0%
Infinite0
Infinite (%)0.0%
Mean39.87419929
Minimum21.3
Maximum57
Zeros0
Zeros (%)0.0%
Memory size6.6 KiB
2021-02-12T10:51:58.704657image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum21.3
5-th percentile29.7
Q136.8
median39.75
Q343.4
95-th percentile49.59
Maximum57
Range35.7
Interquartile range (IQR)6.6

Descriptive statistics

Standard deviation5.770086344
Coefficient of variation (CV)0.1447072655
Kurtosis1.083611259
Mean39.87419929
Median Absolute Deviation (MAD)3.25
Skewness-0.1324485362
Sum22409.3
Variance33.29389642
MonotocityNot monotonic
2021-02-12T10:51:58.822320image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38.313
 
1.6%
4510
 
1.2%
39.29
 
1.1%
409
 
1.1%
39.38
 
1.0%
428
 
1.0%
37.88
 
1.0%
477
 
0.8%
39.87
 
0.8%
41.57
 
0.8%
Other values (186)476
57.6%
(Missing)265
32.0%
ValueCountFrequency (%)
21.31
0.1%
21.51
0.1%
221
0.1%
22.72
0.2%
22.81
0.1%
ValueCountFrequency (%)
571
 
0.1%
56.31
 
0.1%
561
 
0.1%
555
0.6%
542
 
0.2%

hematemesis
Boolean

MISSING

Distinct2
Distinct (%)1.8%
Missing715
Missing (%)86.5%
Memory size6.6 KiB
False
111 
True
 
1
(Missing)
715 
ValueCountFrequency (%)
False111
 
13.4%
True1
 
0.1%
(Missing)715
86.5%
2021-02-12T10:51:58.887384image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

hematoma
Boolean

MISSING

Distinct2
Distinct (%)1.8%
Missing715
Missing (%)86.5%
Memory size6.6 KiB
False
106 
True
 
6
(Missing)
715 
ValueCountFrequency (%)
False106
 
12.8%
True6
 
0.7%
(Missing)715
86.5%
2021-02-12T10:51:58.923185image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

hemoglobin
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct98
Distinct (%)17.5%
Missing266
Missing (%)32.2%
Infinite0
Infinite (%)0.0%
Mean13.36969697
Minimum6.9
Maximum18.8
Zeros0
Zeros (%)0.0%
Memory size6.6 KiB
2021-02-12T10:51:58.997792image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum6.9
5-th percentile10.2
Q112.3
median13.5
Q314.5
95-th percentile16.5
Maximum18.8
Range11.9
Interquartile range (IQR)2.2

Descriptive statistics

Standard deviation1.850311194
Coefficient of variation (CV)0.1383958962
Kurtosis0.899847941
Mean13.36969697
Median Absolute Deviation (MAD)1.1
Skewness-0.3065772585
Sum7500.4
Variance3.423651515
MonotocityNot monotonic
2021-02-12T10:51:59.111750image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.518
 
2.2%
1318
 
2.2%
13.718
 
2.2%
13.918
 
2.2%
12.916
 
1.9%
14.514
 
1.7%
12.514
 
1.7%
12.714
 
1.7%
14.313
 
1.6%
13.813
 
1.6%
Other values (88)405
49.0%
(Missing)266
32.2%
ValueCountFrequency (%)
6.91
0.1%
7.21
0.1%
7.71
0.1%
7.81
0.1%
7.91
0.1%
ValueCountFrequency (%)
18.81
0.1%
18.51
0.1%
18.41
0.1%
17.62
0.2%
17.52
0.2%

igg
Real number (ℝ)

MISSING

Distinct169
Distinct (%)97.7%
Missing654
Missing (%)79.1%
Infinite0
Infinite (%)0.0%
Mean29.31121387
Minimum-0.795
Maximum123.11
Zeros0
Zeros (%)0.0%
Memory size6.6 KiB
2021-02-12T10:51:59.225064image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-0.795
5-th percentile-0.038
Q12.015
median26.11
Q341.795
95-th percentile95.123
Maximum123.11
Range123.905
Interquartile range (IQR)39.78

Descriptive statistics

Standard deviation30.45471737
Coefficient of variation (CV)1.039012492
Kurtosis0.9370534506
Mean29.31121387
Median Absolute Deviation (MAD)23.895
Skewness1.126724323
Sum5070.84
Variance927.4898101
MonotocityNot monotonic
2021-02-12T10:51:59.345210image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1752
 
0.2%
37.6052
 
0.2%
0.4852
 
0.2%
5.1752
 
0.2%
60.781
 
0.1%
0.721
 
0.1%
123.111
 
0.1%
36.581
 
0.1%
53.9751
 
0.1%
15.121
 
0.1%
Other values (159)159
 
19.2%
(Missing)654
79.1%
ValueCountFrequency (%)
-0.7951
0.1%
-0.5051
0.1%
-0.391
0.1%
-0.31
0.1%
-0.291
0.1%
ValueCountFrequency (%)
123.111
0.1%
119.4951
0.1%
119.2051
0.1%
116.9151
0.1%
114.991
0.1%

igg_interpretation
Categorical

MISSING

Distinct3
Distinct (%)1.7%
Missing654
Missing (%)79.1%
Memory size6.6 KiB
Positive
101 
Negative
67 
Equivocal
 
5

Length

Max length9
Median length8
Mean length8.028901734
Min length8

Characters and Unicode

Total characters1389
Distinct characters15
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNegative
2nd rowPositive
3rd rowNegative
4th rowPositive
5th rowNegative
ValueCountFrequency (%)
Positive101
 
12.2%
Negative67
 
8.1%
Equivocal5
 
0.6%
(Missing)654
79.1%
2021-02-12T10:51:59.529798image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-02-12T10:51:59.584718image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
positive101
58.4%
negative67
38.7%
equivocal5
 
2.9%

Most occurring characters

ValueCountFrequency (%)
i274
19.7%
e235
16.9%
v173
12.5%
t168
12.1%
o106
 
7.6%
P101
 
7.3%
s101
 
7.3%
a72
 
5.2%
N67
 
4.8%
g67
 
4.8%
Other values (5)25
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1216
87.5%
Uppercase Letter173
 
12.5%

Most frequent character per category

ValueCountFrequency (%)
i274
22.5%
e235
19.3%
v173
14.2%
t168
13.8%
o106
 
8.7%
s101
 
8.3%
a72
 
5.9%
g67
 
5.5%
q5
 
0.4%
u5
 
0.4%
Other values (2)10
 
0.8%
ValueCountFrequency (%)
P101
58.4%
N67
38.7%
E5
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
Latin1389
100.0%

Most frequent character per script

ValueCountFrequency (%)
i274
19.7%
e235
16.9%
v173
12.5%
t168
12.1%
o106
 
7.6%
P101
 
7.3%
s101
 
7.3%
a72
 
5.2%
N67
 
4.8%
g67
 
4.8%
Other values (5)25
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1389
100.0%

Most frequent character per block

ValueCountFrequency (%)
i274
19.7%
e235
16.9%
v173
12.5%
t168
12.1%
o106
 
7.6%
P101
 
7.3%
s101
 
7.3%
a72
 
5.2%
N67
 
4.8%
g67
 
4.8%
Other values (5)25
 
1.8%

igm
Real number (ℝ≥0)

MISSING

Distinct171
Distinct (%)97.7%
Missing652
Missing (%)78.8%
Infinite0
Infinite (%)0.0%
Mean22.37014286
Minimum0.185
Maximum67.58
Zeros0
Zeros (%)0.0%
Memory size6.6 KiB
2021-02-12T10:51:59.679123image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.185
5-th percentile1.717
Q14.045
median16.3
Q336.8875
95-th percentile59.8105
Maximum67.58
Range67.395
Interquartile range (IQR)32.8425

Descriptive statistics

Standard deviation19.59436142
Coefficient of variation (CV)0.8759157931
Kurtosis-0.8043062565
Mean22.37014286
Median Absolute Deviation (MAD)13.715
Skewness0.649128354
Sum3914.775
Variance383.9389995
MonotocityNot monotonic
2021-02-12T10:51:59.798902image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28.7852
 
0.2%
3.482
 
0.2%
40.522
 
0.2%
6.522
 
0.2%
3.1951
 
0.1%
36.4951
 
0.1%
25.1051
 
0.1%
49.181
 
0.1%
11.311
 
0.1%
2.31
 
0.1%
Other values (161)161
 
19.5%
(Missing)652
78.8%
ValueCountFrequency (%)
0.1851
0.1%
0.1951
0.1%
0.8151
0.1%
1.4151
0.1%
1.421
0.1%
ValueCountFrequency (%)
67.581
0.1%
67.5151
0.1%
64.0951
0.1%
64.011
0.1%
63.281
0.1%

igm_interpretation
Categorical

MISSING

Distinct3
Distinct (%)1.7%
Missing652
Missing (%)78.8%
Memory size6.6 KiB
Positive
101 
Negative
68 
Equivocal
 
6

Length

Max length9
Median length8
Mean length8.034285714
Min length8

Characters and Unicode

Total characters1406
Distinct characters15
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNegative
2nd rowPositive
3rd rowNegative
4th rowPositive
5th rowNegative
ValueCountFrequency (%)
Positive101
 
12.2%
Negative68
 
8.2%
Equivocal6
 
0.7%
(Missing)652
78.8%
2021-02-12T10:51:59.984222image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-02-12T10:52:00.048761image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
positive101
57.7%
negative68
38.9%
equivocal6
 
3.4%

Most occurring characters

ValueCountFrequency (%)
i276
19.6%
e237
16.9%
v175
12.4%
t169
12.0%
o107
 
7.6%
P101
 
7.2%
s101
 
7.2%
a74
 
5.3%
N68
 
4.8%
g68
 
4.8%
Other values (5)30
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1231
87.6%
Uppercase Letter175
 
12.4%

Most frequent character per category

ValueCountFrequency (%)
i276
22.4%
e237
19.3%
v175
14.2%
t169
13.7%
o107
 
8.7%
s101
 
8.2%
a74
 
6.0%
g68
 
5.5%
q6
 
0.5%
u6
 
0.5%
Other values (2)12
 
1.0%
ValueCountFrequency (%)
P101
57.7%
N68
38.9%
E6
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
Latin1406
100.0%

Most frequent character per script

ValueCountFrequency (%)
i276
19.6%
e237
16.9%
v175
12.4%
t169
12.0%
o107
 
7.6%
P101
 
7.2%
s101
 
7.2%
a74
 
5.3%
N68
 
4.8%
g68
 
4.8%
Other values (5)30
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1406
100.0%

Most frequent character per block

ValueCountFrequency (%)
i276
19.6%
e237
16.9%
v175
12.4%
t169
12.0%
o107
 
7.6%
P101
 
7.2%
s101
 
7.2%
a74
 
5.3%
N68
 
4.8%
g68
 
4.8%
Other values (5)30
 
2.1%

lymphocytes_percent
Real number (ℝ≥0)

MISSING

Distinct342
Distinct (%)61.1%
Missing267
Missing (%)32.3%
Infinite0
Infinite (%)0.0%
Mean32.47339286
Minimum2.9
Maximum68.8
Zeros0
Zeros (%)0.0%
Memory size6.6 KiB
2021-02-12T10:52:00.132618image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum2.9
5-th percentile8.485
Q122.575
median33.55
Q342
95-th percentile54.915
Maximum68.8
Range65.9
Interquartile range (IQR)19.425

Descriptive statistics

Standard deviation13.71978407
Coefficient of variation (CV)0.4224930895
Kurtosis-0.5048544961
Mean32.47339286
Median Absolute Deviation (MAD)9.25
Skewness-0.02142260371
Sum18185.1
Variance188.2324751
MonotocityNot monotonic
2021-02-12T10:52:00.253933image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.87
 
0.8%
39.65
 
0.6%
375
 
0.6%
34.65
 
0.6%
19.44
 
0.5%
37.94
 
0.5%
34.84
 
0.5%
47.24
 
0.5%
36.64
 
0.5%
27.34
 
0.5%
Other values (332)514
62.2%
(Missing)267
32.3%
ValueCountFrequency (%)
2.91
0.1%
3.71
0.1%
3.91
0.1%
41
0.1%
4.11
0.1%
ValueCountFrequency (%)
68.81
0.1%
65.91
0.1%
65.31
0.1%
64.71
0.1%
64.31
0.1%

meche
Boolean

MISSING

Distinct2
Distinct (%)1.8%
Missing715
Missing (%)86.5%
Memory size6.6 KiB
False
111 
True
 
1
(Missing)
715 
ValueCountFrequency (%)
False111
 
13.4%
True1
 
0.1%
(Missing)715
86.5%
2021-02-12T10:52:00.322965image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

melaena
Boolean

MISSING

Distinct2
Distinct (%)1.8%
Missing715
Missing (%)86.5%
Memory size6.6 KiB
False
107 
True
 
5
(Missing)
715 
ValueCountFrequency (%)
False107
 
12.9%
True5
 
0.6%
(Missing)715
86.5%
2021-02-12T10:52:00.359131image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

neutrophils_percent
Real number (ℝ≥0)

MISSING

Distinct391
Distinct (%)69.7%
Missing266
Missing (%)32.2%
Infinite0
Infinite (%)0.0%
Mean49.30534759
Minimum10.2
Maximum91.1
Zeros0
Zeros (%)0.0%
Memory size6.6 KiB
2021-02-12T10:52:00.440105image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum10.2
5-th percentile21.2
Q135.7
median48.7
Q362.9
95-th percentile80.7
Maximum91.1
Range80.9
Interquartile range (IQR)27.2

Descriptive statistics

Standard deviation17.95689899
Coefficient of variation (CV)0.3641977973
Kurtosis-0.6867938743
Mean49.30534759
Median Absolute Deviation (MAD)13.4
Skewness0.1956249689
Sum27660.3
Variance322.4502214
MonotocityNot monotonic
2021-02-12T10:52:00.555483image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.45
 
0.6%
54.14
 
0.5%
26.64
 
0.5%
42.74
 
0.5%
46.14
 
0.5%
664
 
0.5%
42.53
 
0.4%
53.33
 
0.4%
63.43
 
0.4%
41.63
 
0.4%
Other values (381)524
63.4%
(Missing)266
32.2%
ValueCountFrequency (%)
10.21
0.1%
111
0.1%
11.21
0.1%
13.91
0.1%
14.61
0.1%
ValueCountFrequency (%)
91.11
0.1%
90.32
0.2%
89.11
0.1%
88.91
0.1%
88.21
0.1%

pcr_dengue_load
Real number (ℝ≥0)

MISSING

Distinct99
Distinct (%)86.8%
Missing713
Missing (%)86.2%
Infinite0
Infinite (%)0.0%
Mean5407925461
Minimum803.57
Maximum1.36429 × 1011
Zeros0
Zeros (%)0.0%
Memory size6.6 KiB
2021-02-12T10:52:00.663822image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum803.57
5-th percentile3828.85
Q1556428.6786
median22071428.79
Q3634285714.2
95-th percentile2.720714296 × 1010
Maximum1.36429 × 1011
Range1.364289992 × 1011
Interquartile range (IQR)633729285.5

Descriptive statistics

Standard deviation2.096683474 × 1010
Coefficient of variation (CV)3.877056903
Kurtosis27.65671495
Mean5407925461
Median Absolute Deviation (MAD)22069916.5
Skewness5.120377362
Sum6.165035026 × 1011
Variance4.396081589 × 1020
MonotocityNot monotonic
2021-02-12T10:52:00.782045image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
95000002
 
0.2%
37785714292
 
0.2%
29500000002
 
0.2%
97142
 
0.2%
14142857142
 
0.2%
49214285712
 
0.2%
160002
 
0.2%
7.285714286 × 10102
 
0.2%
1.685714286 × 10102
 
0.2%
80000000002
 
0.2%
Other values (89)94
 
11.4%
(Missing)713
86.2%
ValueCountFrequency (%)
803.571
0.1%
22211
0.1%
2221.4285711
0.1%
30891
0.1%
3089.291
0.1%
ValueCountFrequency (%)
1.36429 × 10111
0.1%
1.364285714 × 10111
0.1%
7.285714286 × 10102
0.2%
4.642857172 × 10101
0.1%
4.642857172 × 10101
0.1%

pcr_dengue_serotype
Categorical

MISSING

Distinct8
Distinct (%)1.0%
Missing17
Missing (%)2.1%
Memory size6.6 KiB
<LOD
292 
DENV-2
220 
DENV-1
197 
DENV-3
42 
DENV-4
 
26
Other values (3)
33 

Length

Max length13
Median length6
Mean length5.564197531
Min length4

Characters and Unicode

Total characters4507
Distinct characters13
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDENV-2
2nd rowDENV-2
3rd rowDENV-2
4th rowDENV-2
5th rowDENV-2
ValueCountFrequency (%)
<LOD292
35.3%
DENV-2220
26.6%
DENV-1197
23.8%
DENV-342
 
5.1%
DENV-426
 
3.1%
DENV-1,DENV-215
 
1.8%
DENV-1,DENV-410
 
1.2%
DENV-1,DENV-38
 
1.0%
(Missing)17
 
2.1%
2021-02-12T10:52:00.978097image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-02-12T10:52:01.037225image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
lod292
36.0%
denv-2220
27.2%
denv-1197
24.3%
denv-342
 
5.2%
denv-426
 
3.2%
denv-1,denv-215
 
1.9%
denv-1,denv-410
 
1.2%
denv-1,denv-38
 
1.0%

Most occurring characters

ValueCountFrequency (%)
D843
18.7%
E551
12.2%
N551
12.2%
V551
12.2%
-551
12.2%
<292
 
6.5%
L292
 
6.5%
O292
 
6.5%
2235
 
5.2%
1230
 
5.1%
Other values (3)119
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter3080
68.3%
Dash Punctuation551
 
12.2%
Decimal Number551
 
12.2%
Math Symbol292
 
6.5%
Other Punctuation33
 
0.7%

Most frequent character per category

ValueCountFrequency (%)
D843
27.4%
E551
17.9%
N551
17.9%
V551
17.9%
L292
 
9.5%
O292
 
9.5%
ValueCountFrequency (%)
2235
42.6%
1230
41.7%
350
 
9.1%
436
 
6.5%
ValueCountFrequency (%)
-551
100.0%
ValueCountFrequency (%)
<292
100.0%
ValueCountFrequency (%)
,33
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3080
68.3%
Common1427
31.7%

Most frequent character per script

ValueCountFrequency (%)
-551
38.6%
<292
20.5%
2235
16.5%
1230
16.1%
350
 
3.5%
436
 
2.5%
,33
 
2.3%
ValueCountFrequency (%)
D843
27.4%
E551
17.9%
N551
17.9%
V551
17.9%
L292
 
9.5%
O292
 
9.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII4507
100.0%

Most frequent character per block

ValueCountFrequency (%)
D843
18.7%
E551
12.2%
N551
12.2%
V551
12.2%
-551
12.2%
<292
 
6.5%
L292
 
6.5%
O292
 
6.5%
2235
 
5.2%
1230
 
5.1%
Other values (3)119
 
2.6%

petechiae
Boolean

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size955.0 B
False
414 
True
413 
ValueCountFrequency (%)
False414
50.1%
True413
49.9%
2021-02-12T10:52:01.093075image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

plt
Real number (ℝ≥0)

MISSING

Distinct324
Distinct (%)55.3%
Missing241
Missing (%)29.1%
Infinite0
Infinite (%)0.0%
Mean111.2498294
Minimum6
Maximum405
Zeros0
Zeros (%)0.0%
Memory size6.6 KiB
2021-02-12T10:52:01.174831image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile13
Q142.13333333
median88.925
Q3156
95-th percentile289.75
Maximum405
Range399
Interquartile range (IQR)113.8666667

Descriptive statistics

Standard deviation88.82342724
Coefficient of variation (CV)0.7984140538
Kurtosis0.6343433783
Mean111.2498294
Median Absolute Deviation (MAD)54.65
Skewness1.095532639
Sum65192.4
Variance7889.601226
MonotocityNot monotonic
2021-02-12T10:52:01.295536image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
476
 
0.7%
1166
 
0.7%
246
 
0.7%
645
 
0.6%
1125
 
0.6%
305
 
0.6%
485
 
0.6%
235
 
0.6%
495
 
0.6%
195
 
0.6%
Other values (314)533
64.4%
(Missing)241
29.1%
ValueCountFrequency (%)
61
 
0.1%
72
0.2%
81
 
0.1%
94
0.5%
9.21
 
0.1%
ValueCountFrequency (%)
4051
0.1%
3981
0.1%
3951
0.1%
3861
0.1%
3812
0.2%

pt
Categorical

MISSING

Distinct15
Distinct (%)2.7%
Missing262
Missing (%)31.7%
Memory size6.6 KiB
337 
13
74 
14
45 
15
 
30
12
 
30
Other values (10)
49 

Length

Max length2
Median length1
Mean length1.403539823
Min length1

Characters and Unicode

Total characters793
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.9%

Sample

1st row
2nd row14
3rd row
4th row
5th row13
ValueCountFrequency (%)
337
40.7%
1374
 
8.9%
1445
 
5.4%
1530
 
3.6%
1230
 
3.6%
1617
 
2.1%
1710
 
1.2%
117
 
0.8%
186
 
0.7%
194
 
0.5%
Other values (5)5
 
0.6%
(Missing)262
31.7%
2021-02-12T10:52:01.507854image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1374
32.5%
1445
19.7%
1530
13.2%
1230
13.2%
1617
 
7.5%
1710
 
4.4%
117
 
3.1%
186
 
2.6%
194
 
1.8%
221
 
0.4%
Other values (4)4
 
1.8%

Most occurring characters

ValueCountFrequency (%)
337
42.5%
1231
29.1%
374
 
9.3%
446
 
5.8%
236
 
4.5%
531
 
3.9%
617
 
2.1%
711
 
1.4%
86
 
0.8%
94
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number456
57.5%
Space Separator337
42.5%

Most frequent character per category

ValueCountFrequency (%)
1231
50.7%
374
 
16.2%
446
 
10.1%
236
 
7.9%
531
 
6.8%
617
 
3.7%
711
 
2.4%
86
 
1.3%
94
 
0.9%
ValueCountFrequency (%)
337
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common793
100.0%

Most frequent character per script

ValueCountFrequency (%)
337
42.5%
1231
29.1%
374
 
9.3%
446
 
5.8%
236
 
4.5%
531
 
3.9%
617
 
2.1%
711
 
1.4%
86
 
0.8%
94
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII793
100.0%

Most frequent character per block

ValueCountFrequency (%)
337
42.5%
1231
29.1%
374
 
9.3%
446
 
5.8%
236
 
4.5%
531
 
3.9%
617
 
2.1%
711
 
1.4%
86
 
0.8%
94
 
0.5%

serology_interpretation
Categorical

MISSING

Distinct3
Distinct (%)4.3%
Missing757
Missing (%)91.5%
Memory size6.6 KiB
Probably secondary
46 
Inconclusive
15 
Probably primary

Length

Max length18
Median length18
Mean length16.45714286
Min length12

Characters and Unicode

Total characters1152
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowInconclusive
2nd rowInconclusive
3rd rowProbably secondary
4th rowProbably secondary
5th rowProbably secondary
ValueCountFrequency (%)
Probably secondary46
 
5.6%
Inconclusive15
 
1.8%
Probably primary9
 
1.1%
(Missing)757
91.5%
2021-02-12T10:52:01.698522image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-02-12T10:52:01.755213image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
probably55
44.0%
secondary46
36.8%
inconclusive15
 
12.0%
primary9
 
7.2%

Most occurring characters

ValueCountFrequency (%)
r119
10.3%
o116
10.1%
b110
9.5%
a110
9.5%
y110
9.5%
n76
 
6.6%
c76
 
6.6%
l70
 
6.1%
s61
 
5.3%
e61
 
5.3%
Other values (9)243
21.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1027
89.1%
Uppercase Letter70
 
6.1%
Space Separator55
 
4.8%

Most frequent character per category

ValueCountFrequency (%)
r119
11.6%
o116
11.3%
b110
10.7%
a110
10.7%
y110
10.7%
n76
7.4%
c76
7.4%
l70
6.8%
s61
5.9%
e61
5.9%
Other values (6)118
11.5%
ValueCountFrequency (%)
P55
78.6%
I15
 
21.4%
ValueCountFrequency (%)
55
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1097
95.2%
Common55
 
4.8%

Most frequent character per script

ValueCountFrequency (%)
r119
10.8%
o116
10.6%
b110
10.0%
a110
10.0%
y110
10.0%
n76
6.9%
c76
6.9%
l70
 
6.4%
s61
 
5.6%
e61
 
5.6%
Other values (8)188
17.1%
ValueCountFrequency (%)
55
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1152
100.0%

Most frequent character per block

ValueCountFrequency (%)
r119
10.3%
o116
10.1%
b110
9.5%
a110
9.5%
y110
9.5%
n76
 
6.6%
c76
 
6.6%
l70
 
6.1%
s61
 
5.3%
e61
 
5.3%
Other values (9)243
21.1%

shock
Boolean

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size955.0 B
False
521 
True
306 
ValueCountFrequency (%)
False521
63.0%
True306
37.0%
2021-02-12T10:52:01.795941image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size955.0 B
False
728 
True
99 
ValueCountFrequency (%)
False728
88.0%
True99
 
12.0%
2021-02-12T10:52:01.833686image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

wbc
Real number (ℝ≥0)

MISSING

Distinct115
Distinct (%)20.5%
Missing266
Missing (%)32.2%
Infinite0
Infinite (%)0.0%
Mean5.340463458
Minimum1.1
Maximum24.2
Zeros0
Zeros (%)0.0%
Memory size6.6 KiB
2021-02-12T10:52:01.905850image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1.1
5-th percentile1.9
Q13.3
median4.9
Q36.8
95-th percentile10
Maximum24.2
Range23.1
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation2.814164263
Coefficient of variation (CV)0.5269513189
Kurtosis4.842007705
Mean5.340463458
Median Absolute Deviation (MAD)1.7
Skewness1.504701352
Sum2996
Variance7.919520499
MonotocityNot monotonic
2021-02-12T10:52:02.025769image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.613
 
1.6%
3.613
 
1.6%
4.513
 
1.6%
6.112
 
1.5%
4.112
 
1.5%
2.512
 
1.5%
4.312
 
1.5%
5.611
 
1.3%
3.211
 
1.3%
2.811
 
1.3%
Other values (105)441
53.3%
(Missing)266
32.2%
ValueCountFrequency (%)
1.15
0.6%
1.23
0.4%
1.34
0.5%
1.44
0.5%
1.51
 
0.1%
ValueCountFrequency (%)
24.21
0.1%
17.41
0.1%
17.11
0.1%
16.31
0.1%
15.81
0.1%

weight
Real number (ℝ≥0)

MISSING

Distinct36
Distinct (%)4.6%
Missing37
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean50.47848101
Minimum28
Maximum99
Zeros0
Zeros (%)0.0%
Memory size6.6 KiB
2021-02-12T10:52:02.137729image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum28
5-th percentile36
Q142
median48
Q355
95-th percentile72
Maximum99
Range71
Interquartile range (IQR)13

Descriptive statistics

Standard deviation11.47693638
Coefficient of variation (CV)0.2273629505
Kurtosis2.139742517
Mean50.47848101
Median Absolute Deviation (MAD)6
Skewness1.110419022
Sum39878
Variance131.7200687
MonotocityNot monotonic
2021-02-12T10:52:02.239827image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
4572
 
8.7%
4259
 
7.1%
4058
 
7.0%
5057
 
6.9%
5548
 
5.8%
4843
 
5.2%
4742
 
5.1%
7040
 
4.8%
5435
 
4.2%
4627
 
3.3%
Other values (26)309
37.4%
(Missing)37
 
4.5%
ValueCountFrequency (%)
2810
1.2%
3010
1.2%
349
1.1%
3618
2.2%
377
 
0.8%
ValueCountFrequency (%)
998
1.0%
758
1.0%
7410
1.2%
738
1.0%
727
0.8%

day_from_enrolment
Real number (ℝ)

HIGH CORRELATION
MISSING
ZEROS

Distinct129
Distinct (%)16.1%
Missing26
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean16.95131086
Minimum-238
Maximum324
Zeros88
Zeros (%)10.6%
Memory size6.6 KiB
2021-02-12T10:52:02.347957image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-238
5-th percentile-49
Q10
median3
Q37
95-th percentile152
Maximum324
Range562
Interquartile range (IQR)7

Descriptive statistics

Standard deviation59.95816355
Coefficient of variation (CV)3.537081235
Kurtosis7.496124999
Mean16.95131086
Median Absolute Deviation (MAD)3
Skewness2.000215072
Sum13578
Variance3594.981376
MonotocityNot monotonic
2021-02-12T10:52:02.464702image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
088
 
10.6%
271
 
8.6%
370
 
8.5%
159
 
7.1%
458
 
7.0%
558
 
7.0%
658
 
7.0%
-145
 
5.4%
723
 
2.8%
820
 
2.4%
Other values (119)251
30.4%
(Missing)26
 
3.1%
ValueCountFrequency (%)
-2381
 
0.1%
-1771
 
0.1%
-1511
 
0.1%
-1493
0.4%
-1471
 
0.1%
ValueCountFrequency (%)
3241
0.1%
3231
0.1%
2941
0.1%
2931
0.1%
2891
0.1%

day_from_admission
Real number (ℝ)

HIGH CORRELATION
MISSING
ZEROS

Distinct131
Distinct (%)16.4%
Missing26
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean17.8227216
Minimum-238
Maximum325
Zeros88
Zeros (%)10.6%
Memory size6.6 KiB
2021-02-12T10:52:02.570024image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-238
5-th percentile-47
Q11
median4
Q38
95-th percentile152
Maximum325
Range563
Interquartile range (IQR)7

Descriptive statistics

Standard deviation59.97512845
Coefficient of variation (CV)3.365093716
Kurtosis7.487720442
Mean17.8227216
Median Absolute Deviation (MAD)3
Skewness1.99595503
Sum14276
Variance3597.016033
MonotocityNot monotonic
2021-02-12T10:52:02.682858image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
088
 
10.6%
377
 
9.3%
170
 
8.5%
467
 
8.1%
266
 
8.0%
555
 
6.7%
752
 
6.3%
651
 
6.2%
923
 
2.8%
817
 
2.1%
Other values (121)235
28.4%
(Missing)26
 
3.1%
ValueCountFrequency (%)
-2381
 
0.1%
-1771
 
0.1%
-1511
 
0.1%
-1483
0.4%
-1471
 
0.1%
ValueCountFrequency (%)
3251
0.1%
3241
0.1%
2951
0.1%
2941
0.1%
2891
0.1%

Interactions

2021-02-12T10:51:26.008994image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:26.108638image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:26.172045image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:26.243514image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:26.311749image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:26.384808image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:26.475292image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:26.562576image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:26.643099image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:26.717591image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:26.807537image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2021-02-12T10:51:41.615397image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:41.690847image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:41.762536image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:41.829835image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:46.428131image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:46.503267image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:46.570428image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:46.645301image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:46.718628image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:46.793249image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:46.881496image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:46.966336image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:47.027595image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:47.097096image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:47.158809image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:47.232705image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:47.321682image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:47.406621image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:47.472139image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:47.542119image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:47.641137image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:47.732884image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:47.800858image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:47.889302image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:47.980394image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:48.067691image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:48.160819image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:48.245016image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:48.333463image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:48.400467image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:48.471232image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:48.541639image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:48.609463image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:48.694216image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:48.777607image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:48.838949image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:48.905956image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:48.988736image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:49.070574image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:49.146033image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:49.229360image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:49.314270image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:49.404537image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:49.485541image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:49.587036image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:49.686071image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:49.751332image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:49.824346image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:49.887689image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:49.952280image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:50.041518image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:50.122997image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:50.194629image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:50.272240image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:50.357479image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:50.450108image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:50.522813image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:50.628051image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:50.730589image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:50.822923image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:50.910252image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:51.003561image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:51.088897image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:51.150281image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:51.222796image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:51.291601image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:51.365324image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:51.451320image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:51.530416image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:51.603119image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:51.673952image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:51.756925image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:51.845879image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:51.919881image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:52.009746image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:52.087234image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:52.177991image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:52.267745image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:52.359026image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:52.447264image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:52.511558image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:52.578286image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:52.648509image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:52.717721image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:52.802200image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:52.883481image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:52.952991image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:53.031513image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:53.138251image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:53.229569image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:53.304159image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:53.390533image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:53.465870image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:51:53.560353image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2021-02-12T10:52:02.813878image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-02-12T10:52:03.023194image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-02-12T10:52:03.221795image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-02-12T10:52:03.450764image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-02-12T10:51:53.864241image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-02-12T10:51:54.537228image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-02-12T10:51:55.101140image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-02-12T10:51:55.843098image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

study_nodateagealbuminaltapttastbleedingbleeding_gumbleeding_nosebleeding_severitybleeding_vaginalbruisingcreatinineechymoseechymosisevent_serologyfibrinogengenderhaematocrit_percenthematemesishematomahemoglobiniggigg_interpretationigmigm_interpretationlymphocytes_percentmechemelaenaneutrophils_percentpcr_dengue_loadpcr_dengue_serotypepetechiaepltptserology_interpretationshockshock_multiplewbcweightday_from_enrolmentday_from_admission
012010-12-0922.0NaNNaNNaNTrueFalseFalseNaNTrueFalseNaNNaNNaNNaNFemale37.1NaNNaN13.0NaNNaNNaNNaN13.2NaNNaN74.6NaNDENV-2False123.00NaNFalseFalse3.645.0-89.0-88.0
112010-12-1022.0NaNNaN33.2NaNTrueFalseFalseNaNTrueFalseNaNNaNNaNNaN4Female37.7NaNNaN13.4NaNNaNNaNNaN16.8NaNNaN72.5NaNDENV-2False116.0014NaNFalseFalse4.345.0-59.0-58.0
212010-12-1122.0NaNNaNNaNTrueFalseFalseNaNTrueFalseNaNNaNNaNNaNFemale36.8NaNNaN12.9NaNNaNNaNNaN19.7NaNNaN71.6NaNDENV-2False108.00NaNFalseFalse2.445.0-28.0-27.0
312010-12-0922.0NaNNaNNaNNaNNaNFalseFalseNaNTrueFalseNaNNaNNaNNaNNaNFemaleNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNDENV-2FalseNaNNaNNaNFalseFalseNaN45.0-1.00.0
412010-12-1022.0NaNNaN36.2NaNTrueFalseFalse3.0TrueFalseNaNFalseFalseNaN3.7FemaleNaNFalseFalseNaNNaNNaNNaNNaNNaNFalseFalseNaN576428571.4DENV-2False37.15NaNNaNFalseFalseNaN45.00.01.0
512010-12-1222.0NaNNaNNaNTrueFalseFalseNaNTrueFalseNaNNaNNaNNaNFemale33.7NaNNaN12.2NaNNaNNaNNaN18.7NaNNaN59.5NaNDENV-2False77.00NaNFalseFalse2.045.02.03.0
612010-12-1322.0NaNNaN36.2NaNTrueFalseFalseNaNTrueFalseNaNNaNNaNTrue3.7Female34.3NaNNaN12.71.025Negative3.79Negative17.2NaNNaN52.3576428571.0DENV-2False60.0013NaNFalseFalse2.445.03.04.0
712010-12-1422.0NaNNaNNaNTrueFalseFalseNaNTrueFalseNaNNaNNaNNaNFemale34.2NaNNaN12.4NaNNaNNaNNaN23.3NaNNaN38.3NaNDENV-2False64.00NaNFalseFalse3.145.04.05.0
812010-12-1622.0NaNNaNNaNNaNNaNFalseFalseNaNTrueFalseNaNNaNNaNTrueNaNFemaleNaNNaNNaNNaN37.900Positive54.51PositiveNaNNaNNaNNaNNaNDENV-2FalseNaNNaNInconclusiveFalseFalseNaN45.06.07.0
922010-12-0924.0NaNNaNNaNFalseFalseFalseNaNFalseFalseNaNNaNNaNNaNFemale33.0NaNNaN12.2NaNNaNNaNNaN7.5NaNNaN84.4NaNDENV-4False127.00NaNFalseFalse3.634.0-89.0-88.0

Last rows

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8172072011-05-16NaNNaNNaNNaNNaNNaNFalseFalseNaNNaNFalseNaNFalseNaNNaNNaNNaNNaNFalseFalseNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNNaNFalseNaNNaNNaNFalseFalseNaNNaNNaNNaN
8182082011-05-24NaNNaNNaNNaNNaNNaNFalseFalseNaNNaNFalseNaNFalseNaNNaNNaNNaNNaNFalseFalseNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNNaNFalseNaNNaNNaNFalseFalseNaNNaNNaNNaN
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8202102011-06-03NaNNaNNaNNaNNaNNaNFalseFalseNaNNaNFalseNaNFalseNaNNaNNaNNaNNaNFalseFalseNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNNaNFalseNaNNaNNaNFalseFalseNaNNaNNaNNaN
8212112011-06-07NaNNaNNaNNaNNaNNaNFalseFalseNaNNaNFalseNaNFalseNaNNaNNaNNaNNaNFalseFalseNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNNaNFalseNaNNaNNaNFalseFalseNaNNaNNaNNaN
8222212011-04-11NaNNaNNaNNaNNaNNaNFalseFalseNaNNaNTrueNaNFalseNaNNaNNaNNaNNaNFalseFalseNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNNaNFalseNaNNaNNaNFalseFalseNaNNaNNaNNaN
8232222011-04-11NaNNaNNaNNaNNaNNaNFalseFalseNaNNaNFalseNaNFalseNaNNaNNaNNaNNaNFalseFalseNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNNaNFalseNaNNaNNaNFalseFalseNaNNaNNaNNaN
8242232011-04-22NaNNaNNaNNaNNaNNaNFalseFalseNaNNaNFalseNaNFalseNaNNaNNaNNaNNaNFalseFalseNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNNaNFalseNaNNaNNaNFalseFalseNaNNaNNaNNaN
8252242011-05-04NaNNaNNaNNaNNaNNaNFalseFalseNaNNaNTrueNaNFalseNaNNaNNaNNaNNaNFalseFalseNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNNaNFalseNaNNaNNaNFalseFalseNaNNaNNaNNaN
8262252011-05-30NaNNaNNaNNaNNaNNaNFalseFalseNaNNaNFalseNaNFalseNaNNaNNaNNaNNaNFalseFalseNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNNaNFalseNaNNaNNaNFalseFalseNaNNaNNaNNaN